Mankind has attempted to predict human behavior for the duration of recorded history. Early attempts included fortune telling, witchcraft, astrology, “psychic ability”, and “willing” the future to happen. These pseudoscience attempts have been replaced with mathematics and computer-generated models. For example, there has been fair to moderate success in predicting weather, economic factors, political elections, etc., with mathematical models ranging from simple to computer-intensive. However, typical mathematical modeling used for dynamic weather prediction, hurricane prediction, and economic status has not demonstrated accurate and reliable prediction of dynamic human behavior. Although there has been success in predicting effects of some types of behavior, such as predicting college GPA from high-school GPA or from SAT scores, such prediction is based substantially on the simple fact that very smart individuals who have a high GPA in high school or who score well on tests exhibit the same degree of intelligence in the future and score in similar ways. This is not surprising or difficult to predict. If we select dynamic human behavior such as what action a chief executive officer of a company may take, what a specific terrorist group will do next, how a leader may determine actions of a country, where a fugitive may hide, or where a serial murderer may strike next, the task of behavioral prediction is exceedingly complex.
In order to achieve an accurate prediction of complex human behavior, identifying the environmental influences that the individual, group, business, or country responds to is necessary. Once these indicators are identified, they may be subjected to pattern classification methods to identify the complex patterns underlying the occurrence of target behaviors in response to indicators. Using this process, prediction of future behavior can occur if the current presence and absence of the indicators are processed through a trained pattern classifier. To adequately predict human behavior in an automated manner, it is necessary to identify and separate actual predictors of target behaviors from noise variables that appear to be predictors but that generate spurious results. However, conventional behavior prediction models involve significant manual processing and are very time consuming and subject to errors. What is needed is an accurate behavior prediction process that is able to select predictors from noise variables that appear to be predictors but that generate spurious results, and that is capable of identifying patterns that exist among indicators and subsequent behaviors to be predicted in such a manner that accurate prediction is possible.